INTEGRATION OF ARTIFICIAL INTELLIGENCE AND PREDICTIVE ANALYTICS IN POST-MARKETING SURVEILLANCE SYSTEMS

Abstract

The continuous monitoring of pharmaceutical products and medical devices following regulatory approval represents fundamental pillar of public health protection, functioning as the principal safeguard for identifying and mitigating medication-related risks in real-world clinical settings. This comprehensive analysis investigates the contemporary landscape of post-approval safety monitoring frameworks, examining both established regulatory structures and the revolutionary impact of artificial intelligence on drug safety surveillance activities. This review digs into how different countries handle post-marketing surveillance and adverse event reporting—examining approaches from major regulators like the FDA in the US, Europe's EMA, Japan's PMDA, and other important agencies worldwide. What emerges is a mixed picture: while there's some common ground in safety monitoring practices, each region has its own unique spin on implementation. Our analysis reveals that AI technologies can significantly boost traditional PMS methods, excelling at automating signal detection, finding patterns in complex data, and catching adverse events as they happen. We're also seeing predictive analytics mature to the point where it can flag potential safety issues well before they escalate into public health crises. Looking ahead, this review charts the path forward for AI-enhanced PMS systems, examining the regulatory hurdles that need clearing, the practical challenges of getting these systems up and running, and the opportunities for creating safety monitoring that anticipates problems rather than just responding to them. Ultimately, it's about building robust systems that make a real difference in keeping patients.